Content-based Recommender Systems: State of the Art and Trends

By Pasquale Lops, Marco Degemmis, Giovanni Semeraro. 2011

2,032 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, background): “Preference matching has been richly studied in the area of recommender systems, where two kinds of approaches of interest have been developed: 1) content-based approaches [23, 29], where explicit user profiles or item information are used as features, and 2) collaborative filtering approaches where ”
Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews (2016, background): “There has been significant amount of work focused on providing accurate recommendations based on the historical ratings [Pazzani and Billsus, 2007; Sarwar et al., 2001; Bell and Koren, 2007; Noh et al., 2004; Koren et al., 2009; Lops et al., 2011].”
Developing a Price-Sensitive Recommender System to Improve Accuracy and Business Performance of Ecommerce Applications (2015, methodology): “As largely demonstrated by prior research, CB recommender engines suffer from the “over specialization” problem 17 consisting of the fact that the system can only recommend items scoring high against a user’s profile.”
Domain-specific Knowledge Extraction from the Web of Data (2018, methodology): “Most of the existing content-based recommendation systems use the textual description of the items to analyze the properties of the items [Lops et al. 2011].”
Content-Based Collaborative Filtering for News Topic Recommendation (2015, methodology): “Content-based Filtering Content-based recommender systems try to recommend items similar to those a given user has liked in the past(Lops, De Gemmis, and Semeraro 2011).”
Artificial intelligence in recommender systems (2020, background): “The content-based recommender system has several advantages [11, 12].”
Are we really making much progress? A worrying analysis of recent neural recommendation approaches (2019, methodology): “ItemKNN-CBF: Aneighborhoodcontent-based-ﬁltering (CBF)approach with item similarities computed by using item content features (attributes) where vectors f i , f j ∈ R | F | describe the features of items i and j , respectively, and | F | is the number of features.”
Facing the cold start problem in recommender systems (2014, background): “The adoption of CB systems has a number of advantages and disadvantages (Lops et al., 2011).”
Reinforcement Learning based Recommender Systems: A Survey (2021, methodology): “Content-based filtering also has some shortcomings, including limited content analysis, serendipity, and new user [56].”
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, neutral): “2) (Lops et al. 2011, p. 82).”
Recommender Systems (2016, positive): “The works in [5, 376, 477] provide excellent overview articles on content-based recommender systems.”
Making sense of recommendations (2019, background): “, genre, author, and year) along with the ratings data (Gomez‐Uribe & Hunt, 2016; Lops et al., 2011).”
A Deep Framework for Cross-Domain and Cross-System Recommendations (2018, background): “Summary: For CDR, while the existing content-based approaches have difﬁculties in obtaining more user proﬁles and item details [ Lops et al. , 2011 ] , the existing transfer-based approaches require a certain level of overlap between two domains, e.g., common users or items, which restricts them…”
Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels (2017, background): “Content-based recommendation has been criticized by the limited complexity of typically employed features ( limited content analysis ; Lops et al. (2011); Adomavicius and Tuzhilin (2005)).”
Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation (2015, background): “…Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Filtering; I.2.7 [Artificial Intelligence]: Natural Language Processing; H.3.5 [Online Information Services]: Webbased services Keywords Personalized Recommendation; Collaborative Filtering; Sentiment Analysis; Text Mining”
Deep Learning Based Recommender System (2017, background): “Content-based RS generates recommendation lists according to calculate the similarity between user proles and item features [70].”
Linked open data to support content-based recommender systems (2012, background): “One of the aspects in favor of content-based recommender systems compared to those based on collaborative filtering, is its transparency [15].”